Intensive care unit (ICU)-acquired bacteraemia and ICU mortality and discharge: addressing time-varying confounding using appropriate methodology

Pouwels, Koen B; Vansteelandt, Stijn; Batra, Rahul; Edgeworth, Jonathan D; Robotham, Julie V.

doi:10.1016/j.jhin.2017.11.011

Cited by 22 publications

(26 citation statements)

References 25 publications

(40 reference statements)

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“…When it comes to risk factors for increased mortality in patients who acquired a HAI, intubation (OR = 6.7, P < .001) and diabetes mellitus (OR = 2.5, P = .042) were extracted as significant. Surprisingly, mechanical ventilation, which is often identified as a risk factor for HAI mortality, 24 was not identified together with intubation as a predictor in the multivariate analysis. Another interesting finding was the absence of MDR pathogens among risk factors for HAI mortality, since many studies have identified MDR/XDR strains to cause more severe infections with higher mortality rates, 25 while incurring higher hospital costs.…”

Section: Discussionmentioning

confidence: 83%

Hospital-acquired infections in the adult intensive care unit—Epidemiology, antimicrobial resistance patterns, and risk factors for acquisition and mortality

Despotović

Milošević

et al. 2020

American Journal of Infection Control

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Background: Acquisition of Hospital-acquired infections (HAIs) in intensive care units (ICUs) predispose patients to higher mortality rates and additional adverse events. Serbian adult ICUs are rarely investigated for HAIs. The aim of this study was to look into HAIs in an adult ICU and identify risk factors for acquisition of HAIs and mortality. Methods: This retrospective study included 355 patients hospitalized over a 2-year period. Patient characteristics, antimicrobial resistance patterns, and risk factors of acquisition and predictors of mortality in patients who had a HAI were examined. Results: HAIs were diagnosed in 32.7% of patients. Resistance rates > 50% were observed in all antimicrobials except for tigecycline (14%), colistin (9%), and linezolid (0%). Predictors of HAI acquisition were underlying viral CNS infections and invasive devices-urinary and central venous catheters, and nasogastric tubes. Diabetes mellitus and intubation (odds ratio 2.5 and 6.7, P = .042 and <.001) were identified as predictors for increased mortality in patients who had a HAI. Conclusions: Prevalence of HAIs and resistance rates are high compared to ICUs in other European countries. Risk factors for both acquisition of HAI and mortality were identified. Large-scale studies are necessary to look at HAIs in adult ICUs in Serbia.

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Section: Discussionmentioning

confidence: 83%

Hospital-acquired infections in the adult intensive care unit—Epidemiology, antimicrobial resistance patterns, and risk factors for acquisition and mortality

Despotović

Milošević

et al. 2020

American Journal of Infection Control

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“…Many studies suffer from methodological shortcomings that may either underestimate or overestimate of the actual cost of ABR, as has been pointed out previously [14,17,23]. These shortcomings include (1) unmeasured baseline confounding (not adjusting for differences in the characteristics of patients with antibiotic-resistant, antibiotic-susceptible or no infection that affect their clinical outcomes or treatment costs), (2) time-dependent confounding (patients with antibiotic-resistant, antibiotic-susceptible or no infections have different changes to their characteristics such as health between the time of admission and acquiring an infection) [24,25], (3) time-dependent bias (patients with longer stays being more likely to acquire antibiotic-resistant infections) [25], and (4) model misspecification (use of inappropriate models to relate variables to outcomes, such as the use of Cox regression models even though the proportional hazards assumption is rarely valid for cost outcomes) [26]. The literature in this area has shown methodological improvements over time, with greater use of techniques that can correct for time-dependent biases, such as survival models incorporating infection as a time-dependent predictor and multi-state models [29].…”

Section: Hospital-based Studiesmentioning

confidence: 98%

“…The literature in this area has shown methodological improvements over time, with greater use of techniques that can correct for time-dependent biases, such as survival models incorporating infection as a time-dependent predictor and multi-state models [29]. However, few studies adjust for potential time-varying confounding when focusing on hospital-acquired cases, using g-methods such as marginal structural models with inverse probability weighting [25] or nested g-formulae [24]. In contrast to standard regression methods and multistate models, gmethods can provide unbiased estimates of an exposure if there is time-varying confounding that is also affected by the exposure, provided that confounding is accurately measured.…”

Section: Hospital-based Studiesmentioning

confidence: 99%

“…This was supplemented by additional deductive epidemiological and economic reasoning. The deductive reasoning was guided by the literature on causal inference [14,[22][23][24][25][26], perspective and scope of economic impact [27], and opportunity costs [28]. We then conducted a rapid methodological review to (1) extract broad features of ABR cost studies relevant to the methods they use, (2) survey the methods used to cost ABR and (3) examine the extent to which these methods addressed methodological shortcomings.…”

Section: Introductionmentioning

confidence: 99%

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Quantifying the economic cost of antibiotic resistance and the impact of related interventions: rapid methodological review, conceptual framework and recommendations for future studies

Jit

Luangasanatip

et al. 2020

BMC Med

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Background: Antibiotic resistance (ABR) poses a major threat to health and economic wellbeing worldwide. Reducing ABR will require government interventions to incentivise antibiotic development, prudent antibiotic use, infection control and deployment of partial substitutes such as rapid diagnostics and vaccines. The scale of such interventions needs to be calibrated to accurate and comprehensive estimates of the economic cost of ABR.Methods: A conceptual framework for estimating costs attributable to ABR was developed based on previous literature highlighting methodological shortcomings in the field and additional deductive epidemiological and economic reasoning. The framework was supplemented by a rapid methodological review.Results: The review identified 110 articles quantifying ABR costs. Most were based in high-income countries only (91/110), set in hospitals (95/110), used a healthcare provider or payer perspective (97/110), and used matched cohort approaches to compare costs of patients with antibiotic-resistant infections and antibiotic-susceptible infections (or no infection) (87/110). Better use of methods to correct biases and confounding when making this comparison is needed. Findings also need to be extended beyond their limitations in (1) time (projecting present costs into the future), (2) perspective (from the healthcare sector to entire societies and economies), (3) scope (from individuals to communities and ecosystems), and (4) space (from single sites to countries and the world). Analyses of the impact of interventions need to be extended to examine the impact of the intervention on ABR, rather than considering ABR as an exogeneous factor. Conclusions:Quantifying the economic cost of resistance will require greater rigour and innovation in the use of existing methods to design studies that accurately collect relevant outcomes and further research into new techniques for capturing broader economic outcomes.

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“…A detailed description of this procedure is provided in the tutorial by Therneau, Crowson, and Atkinson () and the Appendix of Pouwels et al. (). To obtain appropriate CIs, we propose to use a bootstrap method.…”

Section: A Counterfactual Approach To Define the Population‐attributamentioning

confidence: 99%

The population‐attributable fraction for time‐dependent exposures using dynamic prediction and landmarking

et al. 2019

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The public health impact of a harmful exposure can be quantified by the populationattributable fraction (PAF). The PAF describes the attributable risk due to an exposure and is often interpreted as the proportion of preventable cases if the exposure was extinct. Difficulties in the definition and interpretation of the PAF arise when the exposure of interest depends on time. Then, the definition of exposed and unexposed individuals is not straightforward. We propose dynamic prediction and landmarking to define and estimate a PAF in this data situation. Two estimands are discussed which are based on two hypothetical interventions that could prevent the exposure in different ways. Considering the first estimand, at each landmark the estimation problem is reduced to a time-independent setting. Then, estimation is simply performed by using a generalized-linear model accounting for the current exposure state and further (time-varying) covariates. The second estimand is based on counterfactual outcomes, estimation can be performed using pseudo-values or inverse-probability weights. The approach is explored in a simulation study and applied on two data examples. First, we study a large French database of intensive care unit patients to estimate the population-benefit of a pathogen-specific intervention that could prevent ventilator-associated pneumonia caused by the pathogen Pseudomonas aeruginosa. Moreover, we quantify the population-attributable burden of locoregional and distant recurrence in breast cancer patients. K E Y W O R D Sattributable risk, competing risks, dynamic prediction, landmarking, time-dependent exposure Biometrical Journal. 2020;62:583-597.

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Intensive care unit (ICU)-acquired bacteraemia and ICU mortality and discharge: addressing time-varying confounding using appropriate methodology

Cited by 22 publications

References 25 publications

Hospital-acquired infections in the adult intensive care unit—Epidemiology, antimicrobial resistance patterns, and risk factors for acquisition and mortality

Hospital-acquired infections in the adult intensive care unit—Epidemiology, antimicrobial resistance patterns, and risk factors for acquisition and mortality

Quantifying the economic cost of antibiotic resistance and the impact of related interventions: rapid methodological review, conceptual framework and recommendations for future studies

The population‐attributable fraction for time‐dependent exposures using dynamic prediction and landmarking

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